My long-term objective is to build on my existing track record in understanding the genetic mechanisms that influence differences in pubertal developmental timing and how they impact associated adverse health outcomes. Puberty is a key adolescent developmental milestone that is strongly associated with later life health outcomes, such as poor cardiometabolic health, type 2 diabetes, cancer, and osteoporosis. Hundreds of genetic variants across the genome are known to affect the timing of puberty (assessed by age at menarche in girls and age at voice break in boys), but the sex-specific impact of this genetic variation on health outcomes in adolescence and adulthood remain poorly characterized, particularly for boys. Furthermore, another key puberty trait, the pubertal height growth spurt, is less well characterized genetically, but reflects influences from pubertal timing, height growth potential, and body mass, and deeper investigation may reveal novel insights into the links between puberty and health outcomes. During the mentored K99 phase of this proposal, I will use a machine learning approach to choose the best variants across the genome that predict pubertal timing to build optimized polygenic scores (Aim 1a). These male and female-specific polygenic scores will then be tested for association across hundreds of clinical outcomes in biobanks composed of electronic health records (EHR) in tens of thousands of children and adults (Aim 1b). Subsequently, in the independent R00 phase, I will use causal inference analyses to understand the causal impact of genetically determined pubertal timing on identified health outcomes (Aim 1c). Meanwhile, I will combine what I have learned during Aim 1 with my previous expertise to lead a genome-wide association study of the pubertal height growth spurt in the Early Growth Genetics (EGG) consortium, combining longitudinal data from ~35,000 children across multiple cohorts, followed by trans-ethnic fine-mapping (Aim 2a). Finally, I will apply the pipeline developed in Aim 1 to derive PGS for pubertal growth and determine clinically relevant health outcomes linked to differences in pubertal growth trajectories (Aims 2b-d). In this Pathway to Independence proposal, I will complete training in computational and biomedical informatics techniques to capitalize on large-scale data resources, including linked genetic and EHR data from medical and population-based biobanks and longitudinal adolescent cohorts in the EGG consortium. My mentorship committee is composed of a team of world-renowned experts in genomics, informatics, and computational approaches, who have a strong track record in transitioning postdoctoral fellows into independent researchers. Combining my previous expertise with the proposed research and training objectives, together with a strong mentorship team, the securing of EGG leadership in this phenotypic area, and career development activities, I will be uniquely poised to achieve my goal of transitioning to an independent researcher with the skills needed to advance our understanding of the genetic underpinnings of pubertal development and its linked adult outcomes.